activity
20102026
most citedAn Asymptotic-Preserving method for highly anisotropic elliptic equations based on a micro-macro decomposition

66 citations · 292 across the 31 of their papers we have counts for

collaborators

38 papers

math.NA2026

phi-FD, a second order finite difference scheme for geometries defined by a level-set function: the Neumann case

Michel Duprez, Vanessa Lleras, Alexei Lozinski +2

We extend the phi-FD finite-difference scheme, which handles domains described by a level-set function on a Cartesian grid, to Neumann boundary conditions. The condition is relaxed…

math.NA2026

A ϕ-FEM approach for time-dependent domains with unfitted meshes

Michel Duprez, Johan Marguet, Alexei Lozinski +1

In this work, we propose an unfitted finite element scheme to approximate the solution of the heat equation on moving domains. We use the ϕ-FEM paradigm in which the computational…

math.NA2025

A High-Order Localized Orthogonal Decomposition Method for Heterogeneous Stokes Problems

Moritz Hauck, Alexei Lozinski

In this paper, we propose a high-order extension of the multiscale method introduced by the authors in [SIAM J. Numer. Anal., 63(4) (2025), pp. 1617--1641] for heterogeneous Stokes…

math.NA2025

Gap-SBM: A New Conceptualization of the Shifted Boundary Method with Optimal Convergence for the Neumann and Dirichlet Problems

J. Haydel Collins, Kangan Li, Alexei Lozinski +1

We propose and mathematically analyze a new Shifted Boundary Method for the treatment of Dirichlet and Neumann boundary conditions, with provable optimal accuracy in the - and…

math.NA2025

A Generalized Framework for Higher-Order Localized Orthogonal Decomposition Methods

Moritz Hauck, Alexei Lozinski, Roland Maier

We introduce a generalized framework for studying higher-order versions of the multiscale method known as Localized Orthogonal Decomposition. Through a suitable reformulation, we a…

math.NA2025

Phi-FEM-FNO: a new approach to train a Neural Operator as a fast PDE solver for variable geometries

Michel Duprez, Vanessa Lleras, Alexei Lozinski +2

In this paper, we propose a way to solve partial differential equations (PDEs) by combining machine learning techniques and the finite element method called Phi-FEM. For that, we u…